Multiclass Classification Procedure for Detecting Attacks on MQTT-IoT Protocol by Hector Alaiz-Moreton, Jose Aveleira-Mata, Jorge Ondicol-Garcia, Angel Luis Muñoz-Castañeda, Isaías García, Carmen Benavides

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Machine Learning Model for Detecting Attack in Service Supply Chain by ASMAU OYINLADE OLANIYI, O. A Ayeni, M. G. Adewunmi

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Detecting Attacks and Estimating States of Power Grids from Partial Observations with Machine Learning by Zheng-Meng Zhai, Mohammadamin Moradi, Ying-Cheng Lai

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Reading Lips: An Analytical Framework for Adversarial Passive Detection of Wireless Traffic in IoT Ecosystems by Abdallah Farraj, Eman Hammad

Subjects: “…Traffic detection attacks…”
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Adversarial Drift Detection in Intrusion Detection System by Yaguan Qian, Xiaohui Guan

Subjects: “…intrusion detection;attack traffic;adversarial drift;weighted Rényi distance…”
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Multitier ensemble classifiers for malicious network traffic detection by Jie WANG, Lili YANG, Min YANG

Subjects: “…malicious network traffic;attack detection;attack phase;network flow clustering;ensemble classifier…”
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A Novel Approach to Android Malware Intrusion Detection Using Zero-Shot Learning GANs by Syed Atir Raza Shirazi, Mehwish Shaikh

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Design of advanced intrusion detection systems based on hybrid machine learning techniques in hierarchically wireless sensor networks by Gebrekiros Gebreyesus Gebremariam, J. Panda, S. Indu

“…The proposed system is efficient in simulations for feature extraction and route discovery and detection attacks achieving an accuracy of 99.46%.…”
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Detecting DDoS attack based on compensation non-parameter CUSUM algorithm by YAN Fen1~3, CHEN Yi-qun3, HUANG Hao1, YIN Xin-chun3

“…An effective DDoS attack detection method on target-end network was proposed.The main goal was to detect attack in early stages with few expenditure,and record the suspicious packets in the same time.For DDoS attacks which based on TCP,many unacknowledged segments will be observed in victim end.In every time period ? …”
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Detection of Attacks in Network Traffic with the Autoencoder-Based Unsupervised Learning Method by Yalçın Özkan

“…Solutions based on machine learning algorithms have started to be developed in order to develop appropriate defense systems by detecting attacks in a timely and effective manner. This study focuses on detecting abnormal traffic on networks through deep learning algorithms, and a deep autoencoder model architecture that can be used to detect attacks is recommended. …”
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A Hybrid Mechanism to Detect DDoS Attacks in Software Defined Networks by ÙAfsaneh Banitalebi Dehkordi, MohammadReza Soltanaghaei, Farsad Zamani Boroujeni

“…With the advent of SDN networks, they are not immune to these attacks, and due to the software-centric nature of these networks, this type of attack can be much more difficult for them, ignoring effective parameters such as port and Source IP in detecting attacks, providing costly solutions which are effective in increasing CPU load, and low accuracy in detecting attacks are of the problems of previously presented methods in detecting DDoS attacks. …”
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An Intrusion Detection System Based on Deep Learning and Metaheuristic Algorithm for IOT by Bahman Sanjabi, Mahmood Ahmadi

“…The results showed that the proposed method can detect attacks with a high accuracy of 99%..…”
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CANGuard: An Enhanced Approach to the Detection of Anomalies in CAN-Enabled Vehicles by Damilola Oladimeji, Razaq Jinad, Amar Rasheed, Mohamed Baza

“…In this paper, we propose CANGuard, an Intrusion Detection System (IDS) designed to detect attacks on the CAN network and identify the originating node in real time. …”
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Friends mechanism-based routing intrusion detection model for mobile ad hoc network by Yang XIAO, Lei BAI, Xian WANG

“…The proposed model mainly focused on how to effectively detect routing invasions from mobile ad hoc network as well as how to accurately respond to the malicious nodes,providing a trusted routing environment.A lightweight intrusion detection model was proposed to based on the friends mechanism,taking black hole attack for example,the OPNET software gives the simulation test,and results show that the schemes can effectively detect attacks,it is validated and compared with other conventional models.…”
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A Novel Two-Stage Deep Learning Model for Network Intrusion Detection: LSTM-AE by Vanlalruata Hnamte, Hong Nhung-Nguyen, Jamal Hussain, Yong Hwa-Kim

“…The experimental results show that the proposed hybrid model works well and is applicable for detecting attacks in modern scenarios.…”
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Quantitative method for network security situation based on attack prediction by Hao HU, Run-guo YE, Hong-qi ZHANG, Ying-jie YANG, Yu-ling LIU

“…To predict the attack behaviors accurately and comprehensively as well as to quantify the threat of attack,a quantitative method for network security situation based on attack prediction was proposed.By fusing the situation factors of attacker,defender and network environment,the capability of attacker and the exploitability rate of vulnerability were evaluated utilizing the real-time detected attack events,and the expected time-cost for attack-defense were further calculated.Then an attack prediction algorithm based on the dynamic Bayesian attack graph was designed to infer the follow-up attack actions.At last,the attack threat was quantified as the security risk situation from two levels of the hosts and the overall network.Experimental analysis indicates that the proposed method is suitable for the real adversarial network environment,and is able to predict the occurrence time of attack accurately and quantify the attack threat reasonably.…”
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Fusion of Heterogeneous Intrusion Detection Systems for Network Attack Detection by Jayakumar Kaliappan, Revathi Thiagarajan, Karpagam Sundararajan

“…In proposed Multiple IDS Unit (MIU), there are five IDS units, and each IDS follows a unique algorithm to detect attacks. The feature selection is done with the help of genetic algorithm. …”
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Quantitative method for network security situation based on attack prediction by Hao HU, Run-guo YE, Hong-qi ZHANG, Ying-jie YANG, Yu-ling LIU

“…To predict the attack behaviors accurately and comprehensively as well as to quantify the threat of attack,a quantitative method for network security situation based on attack prediction was proposed.By fusing the situation factors of attacker,defender and network environment,the capability of attacker and the exploitability rate of vulnerability were evaluated utilizing the real-time detected attack events,and the expected time-cost for attack-defense were further calculated.Then an attack prediction algorithm based on the dynamic Bayesian attack graph was designed to infer the follow-up attack actions.At last,the attack threat was quantified as the security risk situation from two levels of the hosts and the overall network.Experimental analysis indicates that the proposed method is suitable for the real adversarial network environment,and is able to predict the occurrence time of attack accurately and quantify the attack threat reasonably.…”
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Anomaly Detection in IoMT Environment Based on Machine Learning: An Overview by Peyman Vafadoost Sabzevar, Hamidreza Rokhsati, Alireza Chamansara, Ahmad Hajipour

“…The purpose of anomaly detection is to develop methods that can detect attackers' attacks and prevent them from happening again. …”
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IMPROVING INTRUSION DETECTION USING TREE ADJOINING GRAMMAR GUIDED GENETIC PROGRAMMING by Vũ Văn Cảnh, Hoàng Tuấn Hảo, Nguyễn Văn Hoàn

“…Compared with previous results, we found that GP and TAG3P are more effective in detecting attacks than previous measures.…”
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